Evidence Base

Systematic mapping of the SQL Server statistics blob — the internal structure that governs query plans but is almost never directly inspected. Demonstrates that critical decision logic can be opaque even inside stable-appearing systems, and that this opacity accumulates as Execution Debt™.

7 teams, 4 months, 15 Plays. 76% of participants reported changing their behaviour. The pilot revealed a clear gap between perceived safety and actual data — teams used that dissonance as a lever for lasting change. Provides direct field evidence for the measurability and reducibility of Human Debt™ in enterprise environments.

3 teams, 4 months, 5 Plays. 44% behaviour change. NPS 6.5. The biggest lever wasn't the software alone — it was giving teams permission and language to do the human work alongside operational work. Demonstrates that Human Debt™ is reducible with structured tooling in operational logistics environments.

Field research from inside accelerated AI execution environments. Human Debt™, Technical Debt™, and AI Execution Debt™ are converging into the same survivability problem. Documents the failure condition and the infrastructure layer required to prevent it — establishing the anchor theorem relating Human Verification Density, Correction Latency, and Accountability Structure Fidelity to Execution Integrity.

A field analysis of black-box decision engines and the accumulation of Execution Debt™. Demonstrates how institutional failure begins with opacity — critical decisions governed by structures no one is looking at directly. Establishes the distinction between monitoring (observing outputs) and inspection (examining governing structure).